http://dl.acm.org/citation.cfm?id=3430697 http://dl.acm.org/citation.cfm?id=3430697 Thu, 03 Dec 2020 00:00:00 GMT 3430697 http://dl.acm.org/citation.cfm?id=3429946
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The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This article contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps, and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps’ algorithmic decision-making. We enriched this data with users’ coping and support strategies through a follow-up online survey (N = 286).]]> Sun, 08 Nov 2020 00:00:00 GMT 3370927 http://dl.acm.org/citation.cfm?id=3369396
Explaining suggestions made by recommendation systems is key to make users trust and accept these systems. This is specially critical in areas such as art image recommendation. Traditionally, artworks are sold in galleries where people can see them physically, and artists have the chance to persuade the people into buying them. On the other side, online art stores only offer the user the action of navigating through the catalog, but nobody plays the persuading role of the artist. Moreover, few works in recommendation systems provide a perspective of the many variables involved in the user perception of several aspects of the system such as domain knowledge, relevance, explainability, and trust.]]> Sun, 08 Nov 2020 00:00:00 GMT 3369396 http://dl.acm.org/citation.cfm?id=3365843
Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform.]]> Sun, 08 Nov 2020 00:00:00 GMT 3365843 http://dl.acm.org/citation.cfm?id=3369397
Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape.]]> Sun, 08 Nov 2020 00:00:00 GMT 3369397 http://dl.acm.org/citation.cfm?id=3370928
Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to be chosen by the users. We present an affect-aware font and color palette selection methodology that aims to facilitate more informed choices. We infer associations of fonts with a set of eight affects, and evaluate the resulting data in a series of user studies both on individual words as well as in word clouds.]]> Sun, 08 Nov 2020 00:00:00 GMT 3370928 http://dl.acm.org/citation.cfm?id=3419764
This article attempts to bridge the gap between widely discussed ethical principles of Human-centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are developed and implemented in multiple organizational structures, I propose 15 recommendations at three levels of governance: team, organization, and industry. The recommendations are intended to increase the reliability, safety, and trustworthiness of HCAI systems: (1) reliable systems based on sound software engineering practices, (2) safety culture through business management strategies, and (3) trustworthy certification by independent oversight. Software engineering practices within teams include audit trails to enable analysis of failures, software engineering workflows, verification and validation testing, bias testing to enhance fairness, and explainable user interfaces.]]> Fri, 16 Oct 2020 00:00:00 GMT 3419764 http://dl.acm.org/citation.cfm?id=3374218
It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent.]]> Fri, 16 Oct 2020 00:00:00 GMT 3374218 http://dl.acm.org/citation.cfm?id=3365842
Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this article, we focus on identifying portraits of soldiers who participated in the American Civil War (1861--65), the first widely photographed conflict. Many thousands of these portraits survive, but only 10%--20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluations of Photo Sleuth one month and 11 months after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution.]]> Fri, 16 Oct 2020 00:00:00 GMT 3365842